Real things everyday people got it to do, and how to try them yourself.
A random mix from the whole library. Shuffle for a new set.
Creator asked the AI for a whole-food crockpot meal plan focused on protein and high fiber. It generated a one-trip grocery list organized by category, a 7-day plan (Chicken Taco Chili, Turkey White Bean Kale Soup, Beef Lentil Stew, Salsa Verde Chicken Quinoa Bowls), and compiled it into a printable PDF for the fridge.
Dan Zhao posted a dark-mode screenshot recommending the AI for auto-insurance shopping, claiming over $1,000/year in savings. The comparison shows six-month premiums: Progressive $1,556 as the new leader vs Costco Connect $2,142.75 and current GEICO $2,324.63.
He does not write code himself. He builds Chrome extensions by giving the AI instructions in Japanese, and 43 of them have shipped. He also says leaning on AI that much, with almost no judgment of his own, is his biggest regret.
The author set the AI on finding the cheapest Broncos Sunday Night Football tickets with seat parameters; it scoured Ticketmaster and resale sites, then kept monitoring pricing every four hours for days. Once the desired tickets appeared, the author completed the purchase himself.
He runs OpenFree, a site that collects open source projects. The AI scans X every day for new projects, filters them, and submits them to his site backend, where a Cloudflare workflow parses each one and sends it to him to approve before it goes live.
The AI and Antigravity took an existing STL and swapped a fan-shroud section for another design by directly manipulating the STL geometry in Python; it went perfectly.
His RemainderBot uses excess the AI and the AI tokens to write PRs from his goals and task lists, 111 PRs done including customer call prep, runs as a cron job (own-use separable from the Show HN tool post).
He left the AI running a long job: transcribe the audio for timecodes, build an edit decision list, and drive ffmpeg to cut a full music video. He came home to a finished edit; in review he still caught alignment bugs, so the result needed his pass before it was final.
Gave the AI samples of his own writing and had it write voice-matching skills, which he now uses for code comments and editing articles and reviews.
An agent researched available tools, built an eval set, and tested tools against each other to pick the set used for his singing app, cutting build time from months or a year to about 10 days.
Marty (MartydudeVR, 6.1K followers), a busy VR content creator, named his agent 'Tom' and asked for a weekly training plan around existing VR cardio on Tuesdays and Thursdays. Phone close-ups show him requesting three weightlifting days scheduled around the cardio; Tom returned a detailed weekly workout plan, asked which days he does cardio, then offered to add the sessions to his calendar.
The open-source 'burner' project gives the AI a real spare Android phone (Android 11+). The agent uses the phone's apps exactly like a human would — opening them, tapping, typing, scrolling — and sends the user screenshots, extending its reach to marketplaces and shopping apps with no website or API.
While the writer's dot diligently flagged payment alerts and delivery notices he already knew about from subject lines, it missed the one message that mattered: his editor at Zapier emailing to ask him to hold off starting an article she had briefed him on. Had he started the work, the missed flag would have wasted real effort.
A man built a chat copilot inside Blender in a day, at a total cost of $7.18, then gave it 5 sentences. In 15 minutes and 52 tool calls it built a spaceship with swept wings, a glass canopy, 4 sets of steering thrusters, and 22 exhaust flames, with a full flight animation. On its own it checked every thruster, found the rear ones firing into the side of the ship, moved them to the wingtips angled outward, and checked them again.
He shipped a ColorBliss feature a customer had asked for and planned to email the customer himself later. When he opened his help desk, his automated support agent had already seen the reopened ticket, checked Linear, confirmed the feature had shipped, and sent the customer a note.
Noah, a Dungeons & Dragons game master, gave his AI Q all the notes from his past sessions plus the group's specific house rules. The AI now helps him prepare and write sessions and pivot mid-game when players make an unexpected choice, using full campaign context. He stays in the driver's seat as DM; he previously used AI for this and found it much weaker.
A non-developer store owner ran Google's PageSpeed tests, printed the expanded results to PDF for both mobile and desktop, and had the AI analyze every red issue and hand back prioritized fixes as cleaned-up code to paste in. His speed score rose 10-15 points and the speed index dropped from 3.2 seconds to 1.2.
His real money trading agent kept saving its own notes until its memory file grew from 4,738 to 65,977 characters in four days. One night it retried a failing step 51 times, burned $36.91, and ate 43% of his whole project API spend.
Eleanor Konik had the AI count the scheduled wakeups across her AI and the AI jobs and found about 300 a week, with roughly half of them wasted. One round of checking, done with AI's help, got the count to about 140 a week with no coverage lost, and about 128 of the old wakeups were just a bot noticing it was 2 AM or a Saturday and going back to sleep.
Rei Derek's Facebook post describes cleaning out his garage for a sale: he gave the AI photos and a price, and the AI autonomously created a live Facebook Marketplace listing — writing the description and correctly identifying the refrigerator's brand from the photo with no prior input.
He built a plugin that lets his agent drive the Sway window manager directly, and used it to switch from X11 to Wayland, something he says did not work for him before. He now asks the agent things like which window is taking the most memory and has it start and stop power profiles.
He says he uses AI to find the cheapest tickets for events he knows he wants to attend. Its feed also told him about an event by a favorite artist of his. The AI bought the tickets, and in a follow up comment he confirms it had him approve the purchase first, which is what he prefers, and handled everything else.
An agent ran a long MongoDB aggregation on a production database replica through a connector tool. The call was moved to the background, and the agent stopped it with the task-stop control, which reported success. The agent told the user the query had been stopped. Only the waiting had stopped: the server-side operation kept running for about 2 hours until it finished on its own. Nobody knew to kill it by hand, because the report said it was already stopped.
AZJedi2000 reports the AI cut his auto insurance by $1,800 a year. He is setting the agent to re-check rates every six months; an AI referral code appears in a follow-up post.
For an eight day trip to Perth with his son, he talked the schedule through with AI and built a travel booklet and two browser apps, one for him and one for his son, with daily phrases and small games for the long flights. The prep took three to four hours, and he says the apps helped them again and again during the trip.
A Reddit user asked the AI to catch up on unfiled vet bills. The AI searched the email backlog, found five bills totalling $1,504.72, and submitted them to the pet insurer.
The AI researched the dimensions for a case he needed and produced a case STL; boring but functional.
His team's agents wrote thousands of files of supposed business logic and decisions as a codebase mental model; it went stale fast and one unreviewed false entry cascaded into misconceptions across whole projects.
He plans his Bluesky posts in a self-hosted Postiz and has connected it to the AI through MCP. He can ask the AI to list the queue and add new drafts, and he approves the drafts himself.
His team puts a few lines in their agent instruction files telling agents to run simple self-verification steps. The agents catch their own errors before committing, and low-effort AI submissions get sent back to the contributor to fix and resubmit. He says it filters out most submissions nobody bothered to look at.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.